Processing Results from DADA2 to make plots, do some statistics

Link to notebook

Link to github repo.

Table of Contents

• Load packages
• Import and prepare the data from eDNA
  • Import metadata
  • Import DADA2 results
  • Make phyloseq object
• QC and filtering eDNA dataset
  • Rarefaction curves
  • Filtering
  • Check sequencing effort
• Abundance plots eDNA
  • Abundance at family level
  • Bubble plots
    • Bubble plot without Elasmobranchs
• Import and prepare the data from trawls
  • Import Trawl Count Data
  • Import CPUE data
• Abundance plots Trawls
  • Bubble plots of Counts
    • Bubble plot without MiFISH species
  • Bubble plots of CPUE
• Compare Trawl and eDNA
• Species Richness
• Species tally across whole study
• Exploratory Analyses
  • Ordinations on eDNA
    • PCA
    • PCoA Jaccard
    • PCoA Bray Curtis
    • NMDS Aitchison
    • NMDS Jacaard
    • NMDS Bray Curtis
    • eDNA Ordinations Summary
  • PCoA with Environmental Variables
    • []

Load packages

library(tidyverse)
library(readxl)
library(phyloseq)
library(Biostrings)
#library(phangorn)
library(readr)
library(seqinr)
library(decontam)
library(ape)
library(vegan)
#library(philr)
library(RColorBrewer)
library(microbiome)
#library(DESeq2)
library(compositions);
library(cowplot)
library(plotly)
library(htmlwidgets)
library(withr)
library(lubridate)

Import and prepare the data from eDNA

Import metadata

metadata <- read_csv("sample_data.csv")

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  SampleID = col_character(),
  `Year.Trawl#` = col_character(),
  Datecode = col_double(),
  Date = col_character(),
  Month = col_double(),
  Year = col_double(),
  Bayside = col_character(),
  Station = col_character(),
  Habitat = col_character(),
  DO = col_double(),
  Salinity = col_double(),
  Temperature = col_double()
)

Import DADA2 results

Import count table and taxonomy file. I slightly modified otutable.csv in Excel to otutable_mod.csv to remove the quotes around seq names and put NA placehoder as first col name (which was above row names)

# Import Count table. Skip first row of tsv file, which is just some text
count_table <- read_table2("results/otutable_mod.csv")

Missing column names filled in: 'X1' [1]
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  X1 = col_character()
)
ℹ Use `spec()` for the full column specifications.

colnames(count_table)[1] <- "SampleID"

# Import taxonomy of ASVs
taxonomy <- read_csv(file="results/tax_sequences_blast_taxonomy.csv")

Missing column names filled in: 'X1' [1]Duplicated column names deduplicated: 'RefSeq_Tax_ID' => 'RefSeq_Tax_ID_1' [18]
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  X1 = col_double(),
  ASV_ID = col_character(),
  ref_seq_ID = col_character(),
  PID = col_double(),
  alnmt_len = col_double(),
  mismatch = col_double(),
  eval = col_double(),
  bscore = col_double(),
  RefSeq_Tax_ID = col_double(),
  Ref_Seq_title = col_character(),
  superkingdom = col_character(),
  phylum = col_character(),
  class = col_character(),
  order = col_character(),
  family = col_character(),
  genus = col_character(),
  species = col_character(),
  RefSeq_Tax_ID_1 = col_double()
)

# remove first col of sequential numbers
taxonomy[,1] <- NULL
# filter out sequences with low PID (recommended by Sara)
taxonomy <- filter(taxonomy, PID > 92)

# remove BLAST metadata and just retain taxonomy (necessary for further processing below)
drop.cols <- c(colnames(taxonomy)[2:9],'RefSeq_Tax_ID_1')
taxonomy <-  select(taxonomy, -one_of(drop.cols))


# And import the Common names, as curated by Sara. Join to taxonomy
commonnames <- read_excel("Trawls MASTER 2020 _mod_ES.xlsx",7)
commonnames

taxonomy <- left_join(taxonomy, commonnames, by = "ASV_ID")
taxonomy

NA

Filtering removed seqs 110, 332 (Gobiosoma ginsburgi and Belone belone) Note for Sara should we consider setting this at 97% which is more robust and still leaves 334 unique ASVs (rather than 379 with the 92% cutoff in the settings above)

Preview datasets

count_table

taxonomy

metadata

Make phyloseq object

I want to use the phyloseq package for some plotting/ statistics, which first requires making phyloseq objects out of each of input data tables-

count_table_matrix <- as.matrix(count_table[,2:392]) # convert count table to matrix, leaving out character column of sample ID
rownames(count_table_matrix) <- count_table$SampleID # add back in Sample IDs as row names
ASV =   otu_table(count_table_matrix, taxa_are_rows =  FALSE)

taxonomy_matrix <- as.matrix(taxonomy[,2:9])
rownames(taxonomy_matrix) <- taxonomy$ASV_ID 
TAX =   tax_table(taxonomy_matrix)

# select only the metada rows with eDNA samples
#metadata_edna <- metadata[complete.cases(metadata[,1]),]
metadata_edna <- metadata %>% filter(!is.na(SampleID))

META    =   sample_data(data.frame(metadata_edna, row.names = metadata_edna$`SampleID`))

First check that the inputs are in compatible formats by checking for ASV names with the phyloseq function, taxa_names

head(taxa_names(TAX))

[1] "Seq_1" "Seq_2" "Seq_3" "Seq_4" "Seq_5" "Seq_6"

head(taxa_names(ASV))

[1] "Seq_1" "Seq_2" "Seq_3" "Seq_4" "Seq_5" "Seq_6"

And check sample names were also detected

# Modify taxa names in ASV, which are formatted with the sample ID, underscor, fastq ID. Don't need this fastq ID anymore and want it to match the sample names from metadata
sample_names(ASV) <-  sample_names(ASV) %>%
  str_replace_all(pattern = "_S[:digit:]+",replacement = "")


head(sample_names(ASV))

[1] "T1PosCon" "T1S10"    "T1S11"    "T1S1"     "T1S2"     "T1S3"    

head(sample_names(META))

[1] "T1PosCon" "T1S1"     "T1S2"     "T1S3"     "T1S5"     "T1S6"    

And make the phyloseq object

ps <- phyloseq(ASV, TAX,    META)

QC and filtering eDNA dataset

Rarefaction curves

rarecurve(otu_table(ps), step=50, cex=0.5)

empty rows removed

# save as .eps
setEPS()
postscript("Figures/rarefaction.eps")

rarecurve(otu_table(ps), step=50, cex=0.5)

empty rows removed

dev.off()

quartz_off_screen 
                2 

Most samples look like they were sampled to completion. Be weary of T3S11, T1S2, and maybe T4S5

Filtering

Check some features of the phyloseq object

rank_names(ps)

[1] "superkingdom" "phylum"       "class"        "order"        "family"       "genus"        "species"      "CommonName"  

unique(tax_table(ps)[, "superkingdom"])

Taxonomy Table:     [2 taxa by 1 taxonomic ranks]:
        superkingdom
Seq_1   "Eukaryota" 
Seq_377 NA          

unique(tax_table(ps)[, "phylum"])

Taxonomy Table:     [3 taxa by 1 taxonomic ranks]:
        phylum      
Seq_1   "Chordata"  
Seq_368 "Arthropoda"
Seq_377 NA          

unique(tax_table(ps)[, "class"])

Taxonomy Table:     [5 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_63  "Mammalia"      
Seq_362 "Chondrichthyes"
Seq_368 "Insecta"       
Seq_377 NA              

There are some ASVs with NA as superkingdom, phylum, or class annotation- delete these.

ps <- subset_taxa(ps, !is.na(superkingdom) & !is.na(phylum) & !is.na(class))

unique(tax_table(ps)[, "superkingdom"])

Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
      superkingdom
Seq_1 "Eukaryota" 

unique(tax_table(ps)[, "phylum"])

Taxonomy Table:     [2 taxa by 1 taxonomic ranks]:
        phylum      
Seq_1   "Chordata"  
Seq_368 "Arthropoda"

unique(tax_table(ps)[, "class"])

Taxonomy Table:     [4 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_63  "Mammalia"      
Seq_362 "Chondrichthyes"
Seq_368 "Insecta"       

nrow(tax_table(ps)) # number of ASVs left

[1] 378

378 ASVs still remain…

Also check class Mammalia, to see if they are contamination or real:

tax_table(subset_taxa(ps, class == 'Mammalia'))

Taxonomy Table:     [8 taxa by 8 taxonomic ranks]:
        superkingdom phylum     class      order          family      genus   species        CommonName 
Seq_63  "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_88  "Eukaryota"  "Chordata" "Mammalia" "Artiodactyla" "Suidae"    "Sus"   "Sus scrofa"   "Wild boar"
Seq_157 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_343 "Eukaryota"  "Chordata" "Mammalia" "Carnivora"    "Felidae"   "Felis" "Felis catus"  "Cat"      
Seq_369 "Eukaryota"  "Chordata" "Mammalia" "Artiodactyla" "Bovidae"   "Bos"   "Bos taurus"   "Cattle"   
Seq_378 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_383 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_389 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    

These are human, wild boar, cat (ahem…cat lady), and cattle. All are contamination so delete all Mammalia

ps <- subset_taxa(ps, !class == 'Mammalia')
unique(tax_table(ps)[, "class"])

Taxonomy Table:     [3 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_362 "Chondrichthyes"
Seq_368 "Insecta"       

Next check the “Insecta” entries

tax_table(subset_taxa(ps, class == 'Insecta'))

Taxonomy Table:     [2 taxa by 8 taxonomic ranks]:
        superkingdom phylum       class     order         family       genus         species              CommonName
Seq_368 "Eukaryota"  "Arthropoda" "Insecta" "Hymenoptera" "Formicidae" "Linepithema" "Linepithema humile" "Ant"     
Seq_380 "Eukaryota"  "Arthropoda" "Insecta" "Hymenoptera" "Formicidae" "Linepithema" "Linepithema humile" "Ant"     

The onlly Insecta is Linepithema humile, which are ants so delete these too..

ps <- subset_taxa(ps, !class == 'Insecta')
unique(tax_table(ps)[, "class"])

Taxonomy Table:     [2 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_362 "Chondrichthyes"

Check sequencing effort

Check overall how many ASVs there are per sample

# First aglomerate the ASVs at the phylum level using the phyloseq function, tax_glom
superkingdomGlommed = tax_glom(ps, "superkingdom")

# and plot
plot_bar(superkingdomGlommed, x = "Sample")

ggsave(filename = "Figures/seqdepth.eps", plot = plot_bar(superkingdomGlommed, x = "Sample"), units = c("in"), width = 9, height = 6, dpi = 300, )# and save

Total sequences reveals certain samples had very low sequencing effort: T1S7, T1S8, T3S11, and, not as bad, T1S2 and T4S5

The rarefaction analysis also showed T1S2 and T4S5 samples were likely not sequenced to completion. Therefore remove these 5 samples from analysis

ps <- subset_samples(ps, !SampleID == "T1S7" & !SampleID == "T1S8" & !SampleID == "T3S11" & !SampleID == "T1S2" & !SampleID == "T4S5")

ps

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 368 taxa and 50 samples ]
sample_data() Sample Data:       [ 50 samples by 12 sample variables ]
tax_table()   Taxonomy Table:    [ 368 taxa by 8 taxonomic ranks ]

50 samples remaining with 368 ASVs

Remove Pos Controls (all hits in positive controls are the same family- I assume this is expected)

ps <- subset_samples(ps, !SampleID == "T1PosCon" & !SampleID == "T2PosCon" & !SampleID == "T3PosCon")
ps

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 368 taxa and 47 samples ]
sample_data() Sample Data:       [ 47 samples by 12 sample variables ]
tax_table()   Taxonomy Table:    [ 368 taxa by 8 taxonomic ranks ]

47 samples remaining with 368 unique ASVs

And lastly, correct some taxonomy: **First* according to Sara, Engraulis encrasicolus (European anchovy) and Engraulis mordax should be Anchoa mitchilli (Bay anchovy):

tax_table(ps) <- gsub(tax_table(ps), pattern = "Engraulis encrasicolus", replacement = "Anchoa mitchilli")  
tax_table(ps) <- gsub(tax_table(ps), pattern = "Engraulis mordax", replacement = "Anchoa mitchilli")  

Second the Fourhorn sculpin (Myoxocephalus quadricornis) is actually an Arctic species. This ASV has 100% PID and 100% query cover to Myoxocephalus quadricornis & Myoxocephalus scorpius (another Arctic species) and 99.4% PID, 100% query cover to Myoxocephalus aenaeus. This latter one is actually the regional species, so this is more likely to be the identity:

tax_table(ps) <- gsub(tax_table(ps), pattern = "Myoxocephalus quadricornis", replacement = "Myoxocephalus aenaeus") 
tax_table(ps) <- gsub(tax_table(ps), pattern = "Fourhorn sculpin", replacement = "Grubby sculpin") 

Third Scomber japonicus, the chub mackerel, is only found in the Indo-Pacific. While this is a commercial product and could be here due to sewage, it is more likely the Scomber colias (Atlantic chub mackerel), which is found regionally (in the open ocean Atlantic). The blast hit to Scomber japonicus has PID of 100% and query cover of 100% while the similarity to Scomber colias 100% query cover/ 99.41% PID.

tax_table(ps) <- gsub(tax_table(ps), pattern = "Scomber japonicus", replacement = "Scomber colias") 
tax_table(ps) <- gsub(tax_table(ps), pattern = "Chub mackerel", replacement = "Atlantic chub mackerel") 

ps

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 368 taxa and 47 samples ]
sample_data() Sample Data:       [ 47 samples by 12 sample variables ]
tax_table()   Taxonomy Table:    [ 368 taxa by 8 taxonomic ranks ]

47 samples remainwith 368 unique ASVs

Abundance plots eDNA

For plotting, use relative abundances (# of ASV sequences/sum total sequences in sample), calculated easily using microbiome::transform

ps_ra <- microbiome::transform(ps, transform = "compositional")

Export the relative abundance matrix so Sara can have it:

# Extract abundance matrix from the phyloseq object
RelAbun_matrix = as(otu_table(ps_ra), "matrix")

# Coerce to data.frame
RelAbun_dataframe = as.data.frame(RelAbun_matrix)

# Export
write.csv(RelAbun_dataframe,"results/otutable_relabun.csv", row.names = TRUE)

Abundance at family level

Then aglomerate the ASVs at the family level using the phyloseq function, tax_glom

familyGlommed_RA = tax_glom(ps_ra, "family")
family_barplot <- plot_bar(familyGlommed_RA, x = "Sample", fill = "family")
family_barplot

NOTES for Sara

• There are some samples, (T1S3, T1S6, T2S11, T3S10, T3S4, T3S5, T3S9, T4S4, T4S7, T5S7) which are composed almost exclusively of 1 family. This might be fine, but I’m not used to seeing this with prokaroytic data. Just want to check with you

Agglomerate by species to see if I get the same 38 unique species Sara sees:

speciesGlommed_RA = tax_glom(ps_ra, "CommonName")
speciesGlommed_RA

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 41 taxa and 47 samples ]
sample_data() Sample Data:       [ 47 samples by 12 sample variables ]
tax_table()   Taxonomy Table:    [ 41 taxa by 8 taxonomic ranks ]

tax_table(speciesGlommed_RA)

Taxonomy Table:     [41 taxa by 8 taxonomic ranks]:
        superkingdom phylum     class            order                family            genus               
Seq_1   "Eukaryota"  "Chordata" "Actinopteri"    "Atheriniformes"     "Atherinopsidae"  "Menidia"           
Seq_2   "Eukaryota"  "Chordata" "Actinopteri"    "Clupeiformes"       "Clupeidae"       "Brevoortia"        
Seq_3   "Eukaryota"  "Chordata" "Actinopteri"    "Clupeiformes"       "Engraulidae"     "Engraulis"         
Seq_4   "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Pomatomidae"     "Pomatomus"         
Seq_5   "Eukaryota"  "Chordata" "Actinopteri"    "Lutjaniformes"      "Lutjanidae"      "Lutjanus"          
Seq_6   "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Paralichthyidae" "Paralichthys"      
Seq_7   "Eukaryota"  "Chordata" "Actinopteri"    "Clupeiformes"       "Clupeidae"       "Alosa"             
Seq_9   "Eukaryota"  "Chordata" "Actinopteri"    "Gobiiformes"        "Gobiidae"        "Gobiosoma"         
Seq_10  "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Scophthalmidae"  "Scophthalmus"      
Seq_11  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Serranidae"      "Centropristis"     
Seq_12  "Eukaryota"  "Chordata" "Actinopteri"    "Spariformes"        "Sparidae"        "Stenotomus"        
Seq_15  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"      "Leiostomus"        
Seq_16  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"      "Menticirrhus"      
Seq_17  "Eukaryota"  "Chordata" "Actinopteri"    "Labriformes"        "Labridae"        "Tautoga"           
Seq_19  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Cottidae"        "Myoxocephalus"     
Seq_20  "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Pleuronectidae"  "Pseudopleuronectes"
Seq_21  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Moronidae"       "Morone"            
Seq_22  "Eukaryota"  "Chordata" "Actinopteri"    "Syngnathiformes"    "Syngnathidae"    "Syngnathus"        
Seq_30  "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Paralichthyidae" "Etropus"           
Seq_33  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"      "Cynoscion"         
Seq_34  "Eukaryota"  "Chordata" "Actinopteri"    "Labriformes"        "Labridae"        "Tautogolabrus"     
Seq_36  "Eukaryota"  "Chordata" "Actinopteri"    "Anguilliformes"     "Anguillidae"     "Anguilla"          
Seq_38  "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"      "Thunnus"           
Seq_40  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Gasterosteidae"  "Apeltes"           
Seq_44  "Eukaryota"  "Chordata" "Actinopteri"    "Cyprinodontiformes" "Fundulidae"      "Fundulus"          
Seq_50  "Eukaryota"  "Chordata" "Actinopteri"    "Atheriniformes"     "Atherinopsidae"  "Membras"           
Seq_52  "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Phycidae"        "Urophycis"         
Seq_54  "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"      "Scomber"           
Seq_57  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Triglidae"       "Prionotus"         
Seq_67  "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"      "Thunnus"           
Seq_82  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"      "Bairdiella"        
Seq_84  "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Gadidae"         "Microgadus"        
Seq_115 "Eukaryota"  "Chordata" "Actinopteri"    "Cyprinodontiformes" "Fundulidae"      "Fundulus"          
Seq_119 "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Phycidae"        "Urophycis"         
Seq_139 "Eukaryota"  "Chordata" "Actinopteri"    "Batrachoidiformes"  "Batrachoididae"  "Opsanus"           
Seq_141 "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"      "Katsuwonus"        
Seq_181 "Eukaryota"  "Chordata" "Actinopteri"    "Tetraodontiformes"  "Tetraodontidae"  "Sphoeroides"       
Seq_231 "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Merlucciidae"    "Merluccius"        
Seq_359 "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Triglidae"       "Prionotus"         
Seq_362 "Eukaryota"  "Chordata" "Chondrichthyes" "Myliobatiformes"    "Myliobatidae"    "Rhinoptera"        
Seq_372 "Eukaryota"  "Chordata" "Chondrichthyes" "Carcharhiniformes"  "Triakidae"       "Mustelus"          
        species                         CommonName                
Seq_1   "Menidia menidia"               "Atlantic silverside"     
Seq_2   "Brevoortia tyrannus"           "Atlantic menhaden"       
Seq_3   "Anchoa mitchilli"              "Bay anchovy"             
Seq_4   "Pomatomus saltatrix"           "Bluefish"                
Seq_5   "Lutjanus griseus"              "Grey snapper"            
Seq_6   "Paralichthys dentatus"         "Summer flounder"         
Seq_7   "Alosa mediocris"               "Hickory shad"            
Seq_9   "Gobiosoma ginsburgi"           "Seaboard goby"           
Seq_10  "Scophthalmus aquosus"          "Windowpane flounder"     
Seq_11  "Centropristis striata"         "Black seabass"           
Seq_12  "Stenotomus chrysops"           "Scup"                    
Seq_15  "Leiostomus xanthurus"          "Spot"                    
Seq_16  "Menticirrhus saxatilis"        "Northern kingfish"       
Seq_17  "Tautoga onitis"                "Tautog"                  
Seq_19  "Myoxocephalus aenaeus"         "Grubby sculpin"          
Seq_20  "Pseudopleuronectes americanus" "Winter flounder"         
Seq_21  "Morone saxatilis"              "Striped bass"            
Seq_22  "Syngnathus fuscus"             "Northern pipefish"       
Seq_30  "Etropus microstomus"           "Smallmouth flounder"     
Seq_33  "Cynoscion regalis"             "Weakfish"                
Seq_34  "Tautogolabrus adspersus"       "Cunner"                  
Seq_36  "Anguilla rostrata"             "American eel"            
Seq_38  "Thunnus obesus"                "Bigeye tuna"             
Seq_40  "Apeltes quadracus"             "Stickleback"             
Seq_44  "Fundulus majalis"              "Striped killifish"       
Seq_50  "Membras martinica"             "Rough silverside"        
Seq_52  "Urophycis floridana"           "Spotted hake"            
Seq_54  "Scomber colias"                "Atlantic chub mackerel"  
Seq_57  "Prionotus carolinus"           "Northern searobin"       
Seq_67  "Thunnus thynnus"               "Atlantic bluefin tuna"   
Seq_82  "Bairdiella chrysoura"          "American silver perch"   
Seq_84  "Microgadus tomcod"             "Atlantic tomcod"         
Seq_115 "Fundulus heteroclitus"         "Mummichog"               
Seq_119 "Urophycis floridana"           "Red hake"                
Seq_139 "Opsanus tau"                   "Oyster toadfish"         
Seq_141 "Katsuwonus pelamis"            "Skipjack tuna"           
Seq_181 "Sphoeroides maculatus"         "Northern puffer"         
Seq_231 "Merluccius bilinearis"         "Silver hake"             
Seq_359 "Prionotus evolans"             "Striped searobin"        
Seq_362 "Rhinoptera bonasus"            "Cownose ray"             
Seq_372 "Mustelus canis"                "Dusky smooth-hound shark"

Bubble plots

Based on my previous scripts with Cariaco Eukaryotic data

# convert ps object to dataframe using phyloseq's psmelt
species_df <- psmelt(speciesGlommed_RA)

# replace zeroes in the table with NA
species_df[species_df == 0] <- NA

# and remove rows with NAs in abundance  (this is so they don't appear as small dots in plot)
species_df <-  filter(species_df, !is.na(Abundance))

Plot by species, scientific name

speciesbubbleplot_eDNA_sciname <- ggplot(species_df, aes(x = Station, y = fct_rev(species), color = Station)) + # the fancy stuff around y (species) helps to present it in reverse order in the plot (from top to btm alphabetically)
  geom_point(aes(size = Abundance, fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(0,.25,.5,.75,1), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Relative Abundance")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_eDNA_sciname

Plot by species common name

speciesbubbleplot_eDNA_comname <- ggplot(species_df, aes(x = Station, y = fct_rev(CommonName), color = Station)) + # the fancy stuff around y (CommonName) helps to present it in reverse order in the plot (from top to btm alphabetically)
  geom_point(aes(size = Abundance, fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(0,.25,.5,.75,1), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Relative Abundance")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_eDNA_comname

Exportfigures

ggsave(filename = "Figures/speciesbubbleplot_eDNA_sciname.eps", plot = speciesbubbleplot_eDNA_sciname, units = c("in"), width = 7, height = 12, dpi = 300)

ggsave(filename = "Figures/speciesbubbleplot_eDNA_comname.eps", plot = speciesbubbleplot_eDNA_comname, units = c("in"), width = 7, height = 12, dpi = 300)

Bubble plot without Elasmobranchs

The above look good but they include two elasmobranchs, the dusky smooth-hound shark and cownose ray. While these are probably real, the MiFISH primers don’t actually target the elasmobranchs, so we can’t trust this assay to fairly represent these non-target species. Filter out and re-make the bubble plots:

ps_no_elasmo <- subset_taxa(ps, !CommonName == 'Cownose ray')
ps_no_elasmo <- subset_taxa(ps_no_elasmo, !CommonName =='Dusky smooth-hound shark')

ps_ra_no_elasmo <- subset_taxa(ps_ra, !CommonName == 'Cownose ray')
ps_ra_no_elasmo <- subset_taxa(ps_ra_no_elasmo, !CommonName =='Dusky smooth-hound shark')

# and check
speciesGlommed_RA_no_elasmo = tax_glom(ps_ra_no_elasmo, "CommonName")
speciesGlommed_RA_no_elasmo

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 39 taxa and 47 samples ]
sample_data() Sample Data:       [ 47 samples by 12 sample variables ]
tax_table()   Taxonomy Table:    [ 39 taxa by 8 taxonomic ranks ]

tax_table(speciesGlommed_RA_no_elasmo)

Taxonomy Table:     [39 taxa by 8 taxonomic ranks]:
        superkingdom phylum     class         order                family            genus               
Seq_1   "Eukaryota"  "Chordata" "Actinopteri" "Atheriniformes"     "Atherinopsidae"  "Menidia"           
Seq_2   "Eukaryota"  "Chordata" "Actinopteri" "Clupeiformes"       "Clupeidae"       "Brevoortia"        
Seq_3   "Eukaryota"  "Chordata" "Actinopteri" "Clupeiformes"       "Engraulidae"     "Engraulis"         
Seq_4   "Eukaryota"  "Chordata" "Actinopteri" "Scombriformes"      "Pomatomidae"     "Pomatomus"         
Seq_5   "Eukaryota"  "Chordata" "Actinopteri" "Lutjaniformes"      "Lutjanidae"      "Lutjanus"          
Seq_6   "Eukaryota"  "Chordata" "Actinopteri" "Pleuronectiformes"  "Paralichthyidae" "Paralichthys"      
Seq_7   "Eukaryota"  "Chordata" "Actinopteri" "Clupeiformes"       "Clupeidae"       "Alosa"             
Seq_9   "Eukaryota"  "Chordata" "Actinopteri" "Gobiiformes"        "Gobiidae"        "Gobiosoma"         
Seq_10  "Eukaryota"  "Chordata" "Actinopteri" "Pleuronectiformes"  "Scophthalmidae"  "Scophthalmus"      
Seq_11  "Eukaryota"  "Chordata" "Actinopteri" "Perciformes"        "Serranidae"      "Centropristis"     
Seq_12  "Eukaryota"  "Chordata" "Actinopteri" "Spariformes"        "Sparidae"        "Stenotomus"        
Seq_15  "Eukaryota"  "Chordata" "Actinopteri" NA                   "Sciaenidae"      "Leiostomus"        
Seq_16  "Eukaryota"  "Chordata" "Actinopteri" NA                   "Sciaenidae"      "Menticirrhus"      
Seq_17  "Eukaryota"  "Chordata" "Actinopteri" "Labriformes"        "Labridae"        "Tautoga"           
Seq_19  "Eukaryota"  "Chordata" "Actinopteri" "Perciformes"        "Cottidae"        "Myoxocephalus"     
Seq_20  "Eukaryota"  "Chordata" "Actinopteri" "Pleuronectiformes"  "Pleuronectidae"  "Pseudopleuronectes"
Seq_21  "Eukaryota"  "Chordata" "Actinopteri" NA                   "Moronidae"       "Morone"            
Seq_22  "Eukaryota"  "Chordata" "Actinopteri" "Syngnathiformes"    "Syngnathidae"    "Syngnathus"        
Seq_30  "Eukaryota"  "Chordata" "Actinopteri" "Pleuronectiformes"  "Paralichthyidae" "Etropus"           
Seq_33  "Eukaryota"  "Chordata" "Actinopteri" NA                   "Sciaenidae"      "Cynoscion"         
Seq_34  "Eukaryota"  "Chordata" "Actinopteri" "Labriformes"        "Labridae"        "Tautogolabrus"     
Seq_36  "Eukaryota"  "Chordata" "Actinopteri" "Anguilliformes"     "Anguillidae"     "Anguilla"          
Seq_38  "Eukaryota"  "Chordata" "Actinopteri" "Scombriformes"      "Scombridae"      "Thunnus"           
Seq_40  "Eukaryota"  "Chordata" "Actinopteri" "Perciformes"        "Gasterosteidae"  "Apeltes"           
Seq_44  "Eukaryota"  "Chordata" "Actinopteri" "Cyprinodontiformes" "Fundulidae"      "Fundulus"          
Seq_50  "Eukaryota"  "Chordata" "Actinopteri" "Atheriniformes"     "Atherinopsidae"  "Membras"           
Seq_52  "Eukaryota"  "Chordata" "Actinopteri" "Gadiformes"         "Phycidae"        "Urophycis"         
Seq_54  "Eukaryota"  "Chordata" "Actinopteri" "Scombriformes"      "Scombridae"      "Scomber"           
Seq_57  "Eukaryota"  "Chordata" "Actinopteri" "Perciformes"        "Triglidae"       "Prionotus"         
Seq_67  "Eukaryota"  "Chordata" "Actinopteri" "Scombriformes"      "Scombridae"      "Thunnus"           
Seq_82  "Eukaryota"  "Chordata" "Actinopteri" NA                   "Sciaenidae"      "Bairdiella"        
Seq_84  "Eukaryota"  "Chordata" "Actinopteri" "Gadiformes"         "Gadidae"         "Microgadus"        
Seq_115 "Eukaryota"  "Chordata" "Actinopteri" "Cyprinodontiformes" "Fundulidae"      "Fundulus"          
Seq_119 "Eukaryota"  "Chordata" "Actinopteri" "Gadiformes"         "Phycidae"        "Urophycis"         
Seq_139 "Eukaryota"  "Chordata" "Actinopteri" "Batrachoidiformes"  "Batrachoididae"  "Opsanus"           
Seq_141 "Eukaryota"  "Chordata" "Actinopteri" "Scombriformes"      "Scombridae"      "Katsuwonus"        
Seq_181 "Eukaryota"  "Chordata" "Actinopteri" "Tetraodontiformes"  "Tetraodontidae"  "Sphoeroides"       
Seq_231 "Eukaryota"  "Chordata" "Actinopteri" "Gadiformes"         "Merlucciidae"    "Merluccius"        
Seq_359 "Eukaryota"  "Chordata" "Actinopteri" "Perciformes"        "Triglidae"       "Prionotus"         
        species                         CommonName              
Seq_1   "Menidia menidia"               "Atlantic silverside"   
Seq_2   "Brevoortia tyrannus"           "Atlantic menhaden"     
Seq_3   "Anchoa mitchilli"              "Bay anchovy"           
Seq_4   "Pomatomus saltatrix"           "Bluefish"              
Seq_5   "Lutjanus griseus"              "Grey snapper"          
Seq_6   "Paralichthys dentatus"         "Summer flounder"       
Seq_7   "Alosa mediocris"               "Hickory shad"          
Seq_9   "Gobiosoma ginsburgi"           "Seaboard goby"         
Seq_10  "Scophthalmus aquosus"          "Windowpane flounder"   
Seq_11  "Centropristis striata"         "Black seabass"         
Seq_12  "Stenotomus chrysops"           "Scup"                  
Seq_15  "Leiostomus xanthurus"          "Spot"                  
Seq_16  "Menticirrhus saxatilis"        "Northern kingfish"     
Seq_17  "Tautoga onitis"                "Tautog"                
Seq_19  "Myoxocephalus aenaeus"         "Grubby sculpin"        
Seq_20  "Pseudopleuronectes americanus" "Winter flounder"       
Seq_21  "Morone saxatilis"              "Striped bass"          
Seq_22  "Syngnathus fuscus"             "Northern pipefish"     
Seq_30  "Etropus microstomus"           "Smallmouth flounder"   
Seq_33  "Cynoscion regalis"             "Weakfish"              
Seq_34  "Tautogolabrus adspersus"       "Cunner"                
Seq_36  "Anguilla rostrata"             "American eel"          
Seq_38  "Thunnus obesus"                "Bigeye tuna"           
Seq_40  "Apeltes quadracus"             "Stickleback"           
Seq_44  "Fundulus majalis"              "Striped killifish"     
Seq_50  "Membras martinica"             "Rough silverside"      
Seq_52  "Urophycis floridana"           "Spotted hake"          
Seq_54  "Scomber colias"                "Atlantic chub mackerel"
Seq_57  "Prionotus carolinus"           "Northern searobin"     
Seq_67  "Thunnus thynnus"               "Atlantic bluefin tuna" 
Seq_82  "Bairdiella chrysoura"          "American silver perch" 
Seq_84  "Microgadus tomcod"             "Atlantic tomcod"       
Seq_115 "Fundulus heteroclitus"         "Mummichog"             
Seq_119 "Urophycis floridana"           "Red hake"              
Seq_139 "Opsanus tau"                   "Oyster toadfish"       
Seq_141 "Katsuwonus pelamis"            "Skipjack tuna"         
Seq_181 "Sphoeroides maculatus"         "Northern puffer"       
Seq_231 "Merluccius bilinearis"         "Silver hake"           
Seq_359 "Prionotus evolans"             "Striped searobin"      

Remake bubble plots. First melt for tidyverse format

# convert ps object to dataframe using phyloseq's psmelt
species_df_no_elasmo <- psmelt(speciesGlommed_RA_no_elasmo)

# replace zeroes in the table with NA
species_df_no_elasmo[species_df_no_elasmo == 0] <- NA

# and remove rows with NAs in abundance  (this is so they don't appear as small dots in plot)
species_df_no_elasmo <-  filter(species_df_no_elasmo, !is.na(Abundance))

Plot by species, scientific name

speciesbubbleplot_eDNA_sciname_no_elasmo <- ggplot(species_df_no_elasmo, aes(x = Station, y = fct_rev(species), color = Station)) + # the fancy stuff around y (species) helps to present it in reverse order in the plot (from top to btm alphabetically)
  geom_point(aes(size = Abundance, fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(0,.25,.5,.75,1), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Relative Abundance")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_eDNA_sciname_no_elasmo

Plot by species common name

speciesbubbleplot_eDNA_comname_no_elasmo <- ggplot(species_df_no_elasmo, aes(x = Station, y = fct_rev(CommonName), color = Station)) + # the fancy stuff around y (CommonName) helps to present it in reverse order in the plot (from top to btm alphabetically)
  geom_point(aes(size = Abundance, fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(0,.25,.5,.75,1), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Relative Abundance")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_eDNA_comname_no_elasmo

Exportfigures

ggsave(filename = "Figures/speciesbubbleplot_eDNA_sciname_no_elasmo.eps", plot = speciesbubbleplot_eDNA_sciname_no_elasmo, units = c("in"), width = 7, height = 12, dpi = 300)

ggsave(filename = "Figures/speciesbubbleplot_eDNA_comname_no_elasmo.eps", plot = speciesbubbleplot_eDNA_comname_no_elasmo, units = c("in"), width = 7, height = 12, dpi = 300)

Import and prepare the data from trawls

Import Trawl Count Data

# import 4th sheet from  Excel file which contains morphometric data for each individual collected for every date

There were 50 or more warnings (use warnings() to see the first 50)

trawl_master <- read_excel("Trawls MASTER 2020 _mod_ES.xlsx",4)
trawl_master

# and import 6th sheet which is station info
stations <- read_excel("Trawls MASTER 2020 _mod_ES.xlsx",6)
stations

Make an equivalent to an OTU table, grouping by date and location and representing counts for every unique species

trawl_counts <- trawl_master %>%
  group_by(DATECODE, STATION_NO, CommonName) %>%
  tally(name = "count")
trawl_counts

and link station names to count table by matching to station number

trawl_counts <- left_join(trawl_counts, stations, by = "STATION_NO")
trawl_counts

Remove 09/16/20 since there is no equivalent eDNA from that date

trawl_counts <- trawl_counts %>%
  filter(DATECODE != "20200916")

Import CPUE data

These have been quality controlled by Sara to remove non-MiFISh species (invertebrates, elasmobranchs) and also to normalize for effort. So the unit is CPUE (catch per unit effort)

# Import
trawl_CPUE <- read_excel("Trawl CPUE no elasmobranch_mod.xlsx", 1)

# Pull out species names
trawl_CPUE_names <- colnames(trawl_CPUE)[3:dim(trawl_CPUE)[2]]

# Change format to long with species as rows
trawl_CPUE <- pivot_longer(trawl_CPUE,cols = colnames(trawl_CPUE)[3:dim(trawl_CPUE)[2]],names_to = "Species", values_to = "CPUE")

# Replace zeroes in CPUE with NA 
trawl_CPUE <- na_if(trawl_CPUE, 0)
# And drop rows with NA (makes plotting easier)
trawl_CPUE  <-  drop_na(trawl_CPUE, CPUE)


# Link station metadata with trawl CPUE data
trawl_CPUE <- left_join(trawl_CPUE, metadata, by = c("Datecode", "Station"))

trawl_CPUE

Abundance plots Trawls

Bubble plots of Counts

speciesbubbleplot_trawl_comname <- ggplot(trawl_counts, aes(x = STATION_NA, y = fct_rev(CommonName), color = STATION_NA)) + 
  geom_point(aes(size = log10(count), fill = STATION_NA), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(log10(1), log10(2), log10(5), log10(10), log10(25), log10(100)), max_size = 6, labels = c("1","2","5","10","25","100"))+
  xlab("")+
  ylab("")+
  labs(size="Abundance", fill = "Station")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(DATECODE~BAYSIDE, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_trawl_comname

Export figure

ggsave(filename = "Figures/speciesbubbleplot_trawl_abundance_comname.eps", plot = speciesbubbleplot_trawl_comname, units = c("in"), width = 6.75, height = 13, dpi = 300)

Bubble plot without MiFISH species

To make a fair comparison, filter out the species from the trawl data that are not targetted by the eDNA MiFISH primers (invertebrates and elasmobrachs)

# Import the list of species that are OK for comparison
MiFISH_targets <- read_excel("Trawl CPUE no elasmobranch_mod.xlsx",2)
MiFISH_targets

Join to trawl data and filter

trawl_counts_MiFISHonly <- left_join(MiFISH_targets,trawl_counts, by = "CommonName")
trawl_counts_MiFISHonly

Reduced the number of rows from 363 to 244.

Plot another bubble plot with only abundance of MiFISh species from the trawl

speciesbubbleplot_trawl_comname_MiFISHonly <- ggplot(trawl_counts_MiFISHonly, aes(x = STATION_NA, y = fct_rev(CommonName), color = STATION_NA)) + 
  geom_point(aes(size = log10(count), fill = STATION_NA), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(log10(1), log10(2), log10(5), log10(10), log10(25), log10(100)), max_size = 6, labels = c("1","2","5","10","25","100"))+
  xlab("")+
  ylab("")+
  labs(size="Abundance", fill = "Station")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(DATECODE~BAYSIDE, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_trawl_comname_MiFISHonly

Export figure

ggsave(filename = "Figures/speciesbubbleplot_trawl_abundance_comname_MiFISHonly.eps", plot = speciesbubbleplot_trawl_comname_MiFISHonly, units = c("in"), width = 6.75, height = 10, dpi = 300)

Bubble plots of CPUE

NOTE: Sara has already filtered the CPUE data to diclude the non-MiFISH species when she calculated CPUE

speciesbubbleplot_trawl_CPUE_comname <- ggplot(trawl_CPUE, aes(x = Station, y = fct_rev(Species), color = Station)) + 
  geom_point(aes(size = log10(CPUE), fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(log10(1), log10(2), log10(5), log10(10), log10(25), log10(100)), max_size = 6, labels = c("1","2","5","10","25","100"))+
  xlab("")+
  ylab("")+
  labs(size="CPUE", fill = "Station")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_trawl_CPUE_comname

Looks good! Similar to “counts” figure but some adjustments that normalized for trawling time.

Export figure

ggsave(filename = "Figures/speciesbubbleplot_trawl_CPUE_comname.eps", plot = speciesbubbleplot_trawl_CPUE_comname, units = c("in"), width = 6.75, height = 13, dpi = 300)

Compare Trawl and eDNA

Species Richness

Count unique species across all stations, grouped by date, for each method, trawl& eDNA (use filtered trawl data so only comparing MiFISh spp to MiFISh spp).

First filter out stations from trawl data that were deleted samples from eDNA analysis because of poor sequencing effort. Filtered eDNA samples: T1S7 (20200707, CORMORANT POINT) T1S8 (20200707, SHINNECOCK HILLS) T3S11 (20200805, LITTLE POND) T1S2 (20200707, WEST MID BAY) T4S5 (20200819, PONQUOGUE BRIDGE)

trawl_counts_MiFISHonly

trawl_counts_MiFISHonly_stationsfiltered <- trawl_counts_MiFISHonly %>%
  filter(!DATECODE == "20200707" | !STATION_NA == "CORMORANT POINT") %>%
  filter(!DATECODE == "20200707" | !STATION_NA == "SHINNECOCK HILLS") %>%
  filter(!DATECODE == "20200805" | !STATION_NA == "LITTLE POND") %>%
  filter(!DATECODE == "20200707" | !STATION_NA == "WEST MID BAY") %>%
  filter(!DATECODE == "20200819" | !STATION_NA == "PONQUOGUE BRIDGE")

trawl_counts_MiFISHonly_stationsfiltered

trawl_uniques <- trawl_counts_MiFISHonly_stationsfiltered %>%
  group_by(DATECODE, CommonName) %>%
  summarise(Trawl_Count = sum(count, na.rm=TRUE))

`summarise()` has grouped output by 'DATECODE'. You can override using the `.groups` argument.

trawl_uniques

eDNA_uniques <- species_df_no_elasmo%>%
  group_by(Datecode, CommonName) %>%
  summarise(eDNA_RelAbun = sum(Abundance, na.rm=TRUE))

`summarise()` has grouped output by 'Datecode'. You can override using the `.groups` argument.

eDNA_uniques

# Combine into one dataframe
trawl_eDNA_abun_table <- full_join(trawl_uniques, eDNA_uniques, by=c("CommonName" = "CommonName", "DATECODE" = "Datecode"))

trawl_eDNA_abun_table

Count total number of species from each method for each date

eDNA_richness <- tally(eDNA_uniques, name = "eDNA")
trawl_richness <- tally(trawl_uniques, name = "trawl")

speciesrichness <- full_join(eDNA_richness, trawl_richness, c("Datecode" = "DATECODE"))
speciesrichness <- pivot_longer(speciesrichness, !Datecode, names_to = "Method", values_to = "Richness")

speciesrichness$Datecode <- ymd(speciesrichness$Datecode) # convert to date format (better for plotting)

speciesrichness

Plot side-by-side

species_richness_plot <- ggplot(speciesrichness, aes(x =Datecode, y = Richness)) +
  geom_line(aes(color = Method), size = 3) +
  theme_bw() +
  xlab("") +
  ylab("Species Richness")

species_richness_plot

# export plot
ggsave(filename = "Figures/species_richness_plot.eps", plot = species_richness_plot, units = c("in"), width = 4, height = 3, dpi = 300)

Sum total number of species across all dates/ stations for entire study

species_sums_abun_table <- trawl_eDNA_abun_table %>%
  group_by(CommonName) %>%
  summarise(Trawl = sum(Trawl_Count, na.rm=TRUE), eDNA = (sum(eDNA_RelAbun, na.rm=TRUE))) %>%
  pivot_longer(!CommonName, names_to = "Method", values_to = "Abundance")
  
# turn zeroes to NA so they don't plot 
species_sums_abun_table <- na_if(species_sums_abun_table,0)

species_sums_abun_table

Species tally across whole study

For each species, plot side-by-side comparison of abundance (summed over whole study) using each method

# First create a custom color scale to make this pretty
myColors <- colorRampPalette(brewer.pal(11,"Spectral"))(40)
names(myColors) <- levels(unique(species_sums_abun_table$CommonName))
colScale <- scale_colour_manual(name = "CommonName",values = myColors)

species_abun_sum_plot <- ggplot(species_sums_abun_table, aes(x = Abundance, y = reorder(CommonName, Abundance, function(x){sum(x,na.rm = TRUE)}), color = CommonName)) +
  geom_point(size = 5) +
  facet_wrap(~fct_rev(Method), scales = "free_x") +
  theme_bw() +
  xlab("Abundance") +
  ylab("") + 
  colScale +
  theme(legend.position = "none")

species_abun_sum_plot

Export plot

ggsave(filename = "Figures/species_abun_sum_plot.eps", plot = species_abun_sum_plot, units = c("in"), width = 5, height = 6, dpi = 300)

Exploratory Analyses

Ordinations on eDNA

I will try PCoA, PCA (the Euclidean PCoA) and NMDS ordinations in combination with different tranformations and distance metrics in order to see which explain the most variance in the dataset.

• NOTE- see this discussion and this paper on why CCA should not be used with CLR-transformed compositional data to explore correlations.

PCA

PCA is essentially a type of PCoA using the Euclidean distance matrix as input. When combined with a log-ratio transformation of the count table, this is deemed appropriate for compositional datasets. It is also recommended as a first step in exploratory analyses of sequencinging datasets.

First do a CLR, centered log ratio transformation of the absolute abundance data (after filtering), as suggested by Gloor et al. 2017

# Estimate covariance matrix for CLR-transformed ASV table
clr_asv_table_ps <- data.frame(compositions::clr(otu_table(ps_no_elasmo)))

Generate the PCA and visualize axes

# Generate a Principle Component Analysis (PCA) and evaluated based on the eigen decomposition from sample covariance matrix. 
lograt_pca <- prcomp(clr_asv_table_ps) 
# NOTE- this is equivalent to first making a Euclidean distance matrix using the CLR data table and then running a PCoA. A Euclidean distance matrix of a log-transformed data table = an Aitchison distance matrix. So this is equivalent to the compositional methods listed in Gloor et al.

# Visual representation with a screeplot
lograt_variances <- as.data.frame(lograt_pca$sdev^2/sum(lograt_pca$sdev^2)) %>% #Extract axes
  # Format to plot
  select(PercVar = 'lograt_pca$sdev^2/sum(lograt_pca$sdev^2)') %>% 
  rownames_to_column(var = "PCaxis") %>% 
  data.frame
head(lograt_variances)

# Plot screeplot
ggplot(lograt_variances, aes(x = as.numeric(PCaxis), y = PercVar)) + 
  geom_bar(stat = "identity", fill = "grey", color = "black") +
  theme_minimal() +
  theme(axis.title = element_text(color = "black", face = "bold", size = 10),
        axis.text.y = element_text(color = "black", face = "bold"),
        axis.text.x = element_blank()) +
  labs(x = "PC axis", y = "% Variance", title = "Log-Ratio PCA Screeplot, CLR Tranformation")

Total variance explained by first three axes= 15.8 + 10.7 + 10.1 = 36.6%. Since the second and third axes are similar, plot in 3D with 3 axes.

Visualize the PCA-

# Extract variances from the clr pca
pca_lograt_frame <- data.frame(lograt_pca$x) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
pca_lograt_frame <- left_join(pca_lograt_frame, metadata, by = "SampleID")
head(pca_lograt_frame)

# Select eigenvalues from dataframe, round to 4 places and multiply by 100 for plotting. These will be the axes for the 3-D plot
eigenvalues<-round(lograt_variances[,2], digits = 4)*100

# Plotly - 3-D
pca_lograt <- plot_ly(pca_lograt_frame, type='scatter3d', mode='markers',
        x=~PC1,y=~PC2,z=~PC3,colors=~brewer.pal(11,'Paired'),
        color=~Station, symbols = c('circle','diamond'), symbol=~Bayside)%>%
  layout(font=list(size=12),
         title='CLR-Euclidean PCA',
         scene=list(xaxis=list(title=paste0('Co 2 ',eigenvalues[2],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    yaxis=list(title=paste0('Co 3 ',eigenvalues[3],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    zaxis=list(title=paste0('Co 1 ',eigenvalues[1],'%'),
                               showticklabels=FALSE,zerolinecolor='black')))
# pca_lograt

# save in "Embedded_figures" directory so that it can be hosted at Github and embedded in this notebook
withr::with_dir('Embedded_figures', htmlwidgets::saveWidget(as_widget(pca_lograt), file="pca_lograt.html", selfcontained = F))

`arrange_()` was deprecated in dplyr 0.7.0.
Please use `arrange()` instead.
See vignette('programming') for more help

Summary The CLR-Euclidean PCA reveals there is some separation according to East vs West. The PCA only explains ~36% of the variance so keep going with different ordinations to see if there is a better representation

PCoA Jaccard

The more traditional approach to ordinations is to do a PCoA on a distance matrix such as Bray-Curtis, Jaccard, or Unifrac. When combined with a transformation, they become more appropriate for NGS data. One such common transformation is the Hellinger transformation.

The different distance matrices also tell you a few different things about the dataset so I will run try different one to try to see if I can tease those out.

Before calculating any distance matrix, do a transformation of the filtered count table. Hellinger transformation is the square root of the relative abundance, so calculate it based on the ps_ra object:

ps_hellinger <- transform_sample_counts(ps_ra_no_elasmo, function(x){sqrt(x)})

First, Jaccard, which builds the distance matrix based on presence/absence between samples. It does not take into account relative abundance of the taxa. Therefore this functions well for determining differences driven by rare taxa, which are weighed the same as abundant taxa.

jac_dmat<-vegdist(otu_table(ps_hellinger),method="jaccard") # Jaccard dist metric
pcoa_jac<-ape::pcoa(jac_dmat) # perform PCoA

# Extract variances from pcoa, from jaccard calculated dist. metric
jac_variances <- data.frame(pcoa_jac$values$Relative_eig) %>% 
  select(PercVar = 'pcoa_jac.values.Relative_eig') %>% 
  rownames_to_column(var = "PCaxis") %>% 
  data.frame
head(jac_variances)

# Make a screeplot
ggplot(jac_variances, aes(x = as.numeric(PCaxis), y = PercVar)) + 
  geom_bar(stat = "identity", fill = "grey", color = "black") +
  theme_minimal() +
  theme(axis.title = element_text(color = "black", face = "bold", size = 10),
        axis.text.y = element_text(color = "black", face = "bold"),
        axis.text.x = element_blank()) +
  labs(x = "PC axis", y = "% Variance", title = "Jaccard PCoA Screeplot")

The first two axes (19.0 + 9.7 = 28.7) are OK. But plot the first 3 axes since the 2nd and 3rd explain a similar amount of variance, (19.0 + 9.7 + 8.4 = 37.1% total variance explained)

Plot in 3D with Plotly

# Extract variances from the jaccard pcoa
pcoa_jac_df <- data.frame(pcoa_jac$vectors) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
pcoa_jac_df <- left_join(pcoa_jac_df, metadata, by = "SampleID")
head(pcoa_jac_df)

# Select eigenvalues from dataframe, round to 4 places and multiply by 100 for plotting. These will be the axes for the 3-D plot
eigenvalues<-round(jac_variances[,2], digits = 4)*100

# Plotly - 3-D
pcoa_jaccard <- plot_ly(pcoa_jac_df, type='scatter3d', mode='markers',
        x=~Axis.2,y=~Axis.3,z=~Axis.1,colors=~brewer.pal(11,'Paired'),
        color=~Station, symbols = c('circle','diamond'), symbol=~Bayside)%>%
  layout(font=list(size=12),
         title='PCoA Jaccard Distance',
         scene=list(xaxis=list(title=paste0('Co 2 ',eigenvalues[2],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    yaxis=list(title=paste0('Co 3 ',eigenvalues[3],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    zaxis=list(title=paste0('Co 1 ',eigenvalues[1],'%'),
                               showticklabels=FALSE,zerolinecolor='black')))
# pcoa_jaccard

# save figure in "Embedded_figures" directory so that it can be hosted at Github and embedded in this notebook
withr::with_dir('Embedded_figures', htmlwidgets::saveWidget(as_widget(pcoa_jaccard), file="pcoa_jaccard.html", selfcontained = F))

The Jaccard-PCoA shows some separation along axis 2 and axis 3 in East vs West differences. Very similar % variance explained to the PCA.

PCoA Bray Curtis

Next, try a Bray-Curtis distance matrix with PCoA, which builds the distance matrix based on presence/absence between samples and relative abundance differences. This ordination will represent well the differences in samples that are driven by taxa with high relative abundances.

NOTE: I need to use a correction here for negative eigenvalues. Read more here

bray_dmat<-vegdist(otu_table(ps_hellinger),method="bray") # Bray-Curtis dist metric
pcoa_bray<-ape::pcoa(bray_dmat) # perform PCoA in ape. But getting negative eigenvalues, so need to add correction. wcmdscale from base R also performs PCoA and can add cailliez correction
pcoa_bray <- wcmdscale(bray_dmat, eig = TRUE, add = "cailliez")

# check out summary of PCoA
eigenvals(pcoa_bray) %>%
  summary() -> ev
ev

Importance of components:
                        [,1]   [,2]    [,3]    [,4]    [,5]    [,6]    [,7]    [,8]    [,9]   [,10]   [,11]
Eigenvalue            6.3479 3.3005 2.85957 1.62805 1.33439 1.24855 1.00938 0.90346 0.87311 0.77992 0.71218
Proportion Explained  0.2112 0.1098 0.09512 0.05416 0.04439 0.04153 0.03358 0.03005 0.02904 0.02594 0.02369
Cumulative Proportion 0.2112 0.3210 0.41608 0.47023 0.51462 0.55615 0.58973 0.61978 0.64883 0.67477 0.69846
                        [,12]   [,13]   [,14]  [,15]   [,16]   [,17]   [,18]  [,19]   [,20]   [,21]   [,22]
Eigenvalue            0.65613 0.60610 0.54826 0.4989 0.44174 0.40567 0.39186 0.3667 0.34891 0.33706 0.33146
Proportion Explained  0.02183 0.02016 0.01824 0.0166 0.01469 0.01349 0.01304 0.0122 0.01161 0.01121 0.01103
Cumulative Proportion 0.72029 0.74045 0.75869 0.7753 0.78998 0.80347 0.81651 0.8287 0.84031 0.85152 0.86255
                        [,23]    [,24]    [,25]    [,26]    [,27]    [,28]    [,29]    [,30]    [,31]    [,32]
Eigenvalue            0.30199 0.284949 0.268734 0.255852 0.247955 0.239199 0.225418 0.217687 0.198673 0.194406
Proportion Explained  0.01005 0.009479 0.008939 0.008511 0.008248 0.007957 0.007498 0.007241 0.006609 0.006467
Cumulative Proportion 0.87259 0.882071 0.891010 0.899521 0.907769 0.915726 0.923224 0.930465 0.937074 0.943541
                         [,33]    [,34]    [,35]    [,36]    [,37]    [,38]    [,39]   [,40]   [,41]    [,42]
Eigenvalue            0.186256 0.166417 0.156276 0.152618 0.150887 0.139347 0.133210 0.12775 0.12414 0.110383
Proportion Explained  0.006196 0.005536 0.005198 0.005077 0.005019 0.004635 0.004431 0.00425 0.00413 0.003672
Cumulative Proportion 0.949737 0.955273 0.960471 0.965548 0.970567 0.975202 0.979634 0.98388 0.98801 0.991685
                         [,43]    [,44]    [,45]
Eigenvalue            0.106401 0.085699 0.057876
Proportion Explained  0.003539 0.002851 0.001925
Cumulative Proportion 0.995224 0.998075 1.000000

# extract variances and put in tibble
bray_variances <- NULL
for (i in 1:length(eigenvals(pcoa_bray))){
  bray_variances[i] <- eigenvals(pcoa_bray)[i]/sum(eigenvals(pcoa_bray))
}

# Extract variances from pcoa, from calculated dist. metric
bray_variances <- tibble(round(bray_variances,3)) %>%
  select(PercVar = 'round(bray_variances, 3)') %>%
  rownames_to_column(var = "PCaxis") %>%
  data.frame
head(bray_variances)

# Make a screeplot
ggplot(bray_variances, aes(x = as.numeric(PCaxis), y = PercVar)) + 
  geom_bar(stat = "identity", fill = "grey", color = "black") +
  theme_minimal() +
  theme(axis.title = element_text(color = "black", face = "bold", size = 10),
        axis.text.y = element_text(color = "black", face = "bold"),
        axis.text.x = element_blank()) +
  labs(x = "PC axis", y = "% Variance", title = "Bray-Curtis PCoA Screeplot")

The first two axes (21.1 + 11.0) are pretty good again but I am still going to experiment in the plot with the 3rd axis since it is similar to the second (9.5%; total variance explained = 41.6%)

Plot in 3D with Plotly

# Extract variances from the pcoa

There were 22 warnings (use warnings() to see them)

pcoa_bray_df <- data.frame(pcoa_bray$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
pcoa_bray_df <- left_join(pcoa_bray_df, metadata, by = "SampleID")
head(pcoa_bray_df)

# Select eigenvalues from dataframe, round to 4 places and multiply by 100 for plotting. These will be the axes for the 3-D plot
eigenvalues<-round(bray_variances[,2], digits = 4)*100

# Plotly - 3-D
pcoa_bray <- plot_ly(pcoa_bray_df, type='scatter3d', mode='markers', 
                     x=~Dim2, y=~Dim3, z=~Dim1, colors=~brewer.pal(11,'Paired'),
        color=~Station, symbols = c('circle','diamond'), symbol=~Bayside)%>%  
  layout(font=list(size=12),
         title='PCoA Bray-Curtis Distance',
         scene=list(xaxis=list(title=paste0('Co 2 ',eigenvalues[2],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    yaxis=list(title=paste0('Co 3 ',eigenvalues[3],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    zaxis=list(title=paste0('Co 1 ',eigenvalues[1],'%'),
                               showticklabels=FALSE,zerolinecolor='black')))
# pcoa_bray

# save in "Embedded_figures" directory so that it can be hosted at Github and embedded in this notebook
withr::with_dir('Embedded_figures', htmlwidgets::saveWidget(as_widget(pcoa_bray), file="pcoa_bray.html", selfcontained = F))

These results along axes 1, 2, and 3 are similar to Jaccard, but there is MORE separation along axis 2, indicating that incorporating the differences in abundance helps explain more variance in the dataset. Total variance explained is highest so far.

NMDS Aitchison

Lastly, try a non-metric dimensional scaling ordination. PCA/PCoA are metric and attempt to rotate axes to fit the distance matrix distribution. An NMDS represents the data in 2-axes, by constraining the distribution of the points. Similar to above, this can be combined with different pre-treatment of the data.

First try the compositional approach, an NMDS on CLR-tranformed data using the Euclidean distances (aka Aitchison distance)

There were 22 warnings (use warnings() to see them)

euc_dmat<-dist(clr_asv_table_ps, method = "euclidean") # Build the Aitchison distance matrix
euc_nmds <- metaMDS(euc_dmat, k=2, autotransform=FALSE) # Run the ordination

Run 0 stress 0.2095936 
Run 1 stress 0.2100444 
... Procrustes: rmse 0.0218251  max resid 0.1004627 
Run 2 stress 0.2136311 
Run 3 stress 0.2325 
Run 4 stress 0.2095881 
... New best solution
... Procrustes: rmse 0.0192675  max resid 0.05983752 
Run 5 stress 0.2239151 
Run 6 stress 0.2120968 
Run 7 stress 0.2325911 
Run 8 stress 0.2144329 
Run 9 stress 0.2295577 
Run 10 stress 0.2405633 
Run 11 stress 0.2400077 
Run 12 stress 0.218828 
Run 13 stress 0.232367 
Run 14 stress 0.2123688 
Run 15 stress 0.211756 
Run 16 stress 0.2153388 
Run 17 stress 0.2126682 
Run 18 stress 0.2096237 
... Procrustes: rmse 0.01862504  max resid 0.06155368 
Run 19 stress 0.2126773 
Run 20 stress 0.2302338 
*** No convergence -- monoMDS stopping criteria:
     2: no. of iterations >= maxit
    18: stress ratio > sratmax

euc_nmds$stress #Check the stress. Less than 0.1 is good. Less than 0.05 is better. This will be different each time, since it is iteratively finding a unique solution each time (although the should look similar)

[1] 0.2095881

# Extract points from nmds and merge into data frame with metadata 
euc_nmds_df <- data.frame(euc_nmds$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
euc_nmds_df <- left_join(euc_nmds_df, metadata, by = "SampleID")
head(euc_nmds_df)

## Plotting euclidean distance NMDS
nmds_aitch <- ggplot(euc_nmds_df,aes(x = MDS1, y = MDS2, color = Station, shape = Bayside)) +
  geom_point(size = 4) +
  scale_color_brewer(palette="Paired") +
  theme_bw() +
  labs(x = "NMDS 1", y = "NMDS 2", title = paste0('Aitchison Distance NMDS, Stress = ', round(euc_nmds$stress,2))) +
  coord_fixed(ratio = 1)

nmds_aitch

ggsave("figures/nmds_aitch.eps",nmds_aitch, width = 7, height = 5, units = c("in"))

The above has a relatively high stress (>0.2) so should be interpreted with caution. But it does show some separation East vs West along NMDS 1.

NMDS Jacaard

Next try a Jaccard NMDS, which will represent differences in presence/absence among samples, emphasizing both abundant and rare taxa the same

jac_nmds <- metaMDS(jac_dmat, k=2, autotransform=FALSE) # Run the ordination. Distance matrix was already calculated above

Run 0 stress 0.1625677 
Run 1 stress 0.1634137 
Run 2 stress 0.1570634 
... New best solution
... Procrustes: rmse 0.08425929  max resid 0.3242781 
Run 3 stress 0.1627786 
Run 4 stress 0.1496462 
... New best solution
... Procrustes: rmse 0.05711037  max resid 0.323175 
Run 5 stress 0.1495058 
... New best solution
... Procrustes: rmse 0.01248582  max resid 0.07437493 
Run 6 stress 0.1496305 
... Procrustes: rmse 0.01229258  max resid 0.07428191 
Run 7 stress 0.164882 
Run 8 stress 0.1508383 
Run 9 stress 0.1835915 
Run 10 stress 0.1634788 
Run 11 stress 0.1568753 
Run 12 stress 0.1568754 
Run 13 stress 0.1625678 
Run 14 stress 0.1760337 
Run 15 stress 0.1662859 
Run 16 stress 0.1640239 
Run 17 stress 0.1496301 
... Procrustes: rmse 0.01229466  max resid 0.07415229 
Run 18 stress 0.1935237 
Run 19 stress 0.1496458 
... Procrustes: rmse 0.01249468  max resid 0.07420985 
Run 20 stress 0.191093 
*** No convergence -- monoMDS stopping criteria:
     1: no. of iterations >= maxit
    19: stress ratio > sratmax

jac_nmds$stress #Check the stress. Less than 0.1 is good. Less than 0.5 is better. This will be different each time, since it is iteratively finding a unique solution each time (although the should look similar)

[1] 0.1495058

# Extract points from nmds and merge into data frame with metadata 
jac_nmds_df <- data.frame(jac_nmds$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
jac_nmds_df <- left_join(jac_nmds_df, metadata, by = "SampleID")
head(jac_nmds_df)

## Plotting euclidean distance NMDS
nmds_jaccard <- ggplot(jac_nmds_df,aes(x = MDS1, y = MDS2, color = Station, shape = Bayside)) +
  geom_point(size = 4) +
  scale_color_brewer(palette="Paired") +
  theme_bw() +
  labs(x = "NMDS 1", y = "NMDS 2", title = paste0('Jaccard Distance NMDS, Stress = ', round(jac_nmds$stress,2))) +
  coord_fixed(ratio = 1)

nmds_jaccard

ggsave("figures/nmds_jaccard.eps",nmds_jaccard, width = 7, height = 5, units = c("in"))

This is still a moderately high stress (>0.1) so should be interpreted with caution. Similar to Aitchison-distance nMDS but there is a little more separation of East vs West on NMDS 2 axis.

NMDS Bray Curtis

Next try a Bray-Curis NMDS, which will represent differences in presence/absence among samples and relative abundance, thus emphasizing impacts of highly abundant taxa.

bray_nmds <- metaMDS(bray_dmat, k=2, autotransform=FALSE) # Run the ordination. Distance matrix was already calculated above

Run 0 stress 0.1628464 
Run 1 stress 0.1573701 
... New best solution
... Procrustes: rmse 0.07657984  max resid 0.3294186 
Run 2 stress 0.1703774 
Run 3 stress 0.1802019 
Run 4 stress 0.1498108 
... New best solution
... Procrustes: rmse 0.03829183  max resid 0.1552968 
Run 5 stress 0.149506 
... New best solution
... Procrustes: rmse 0.05251502  max resid 0.3264717 
Run 6 stress 0.194992 
Run 7 stress 0.1498108 
... Procrustes: rmse 0.05250922  max resid 0.328034 
Run 8 stress 0.1743275 
Run 9 stress 0.1495061 
... Procrustes: rmse 8.023575e-05  max resid 0.0004186855 
... Similar to previous best
Run 10 stress 0.1651077 
Run 11 stress 0.1963769 
Run 12 stress 0.170378 
Run 13 stress 0.1996811 
Run 14 stress 0.1764878 
Run 15 stress 0.1633417 
Run 16 stress 0.1761644 
Run 17 stress 0.1496303 
... Procrustes: rmse 0.01250165  max resid 0.07576832 
Run 18 stress 0.1511936 
Run 19 stress 0.1700407 
Run 20 stress 0.1921941 
*** Solution reached

There were 22 warnings (use warnings() to see them)

bray_nmds$stress #Check the stress. Less than 0.1 is good. Less than 0.5 is better. This will be different each time, since it is iteratively finding a unique solution each time (although the should look similar)

[1] 0.149506

# Extract points from nmds and merge into data frame with metadata 
bray_nmds_df <- data.frame(bray_nmds$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
bray_nmds_df <- left_join(bray_nmds_df, metadata, by = "SampleID")
head(bray_nmds_df)

## Plotting euclidean distance NMDS
nmds_bray <- ggplot(bray_nmds_df,aes(x = MDS1, y = MDS2, color = Station, shape = Bayside)) +
  geom_point(size = 4) +
  scale_color_brewer(palette="Paired") +
  theme_bw() +
  labs(x = "NMDS 1", y = "NMDS 2", title = paste0('Bray-Curtis Distance NMDS, Stress = ', round(bray_nmds$stress,2))) +
  coord_fixed(ratio = 1)

nmds_bray

ggsave("figures/nmds_bray.eps",nmds_bray, width = 7, height = 5, units = c("in"))

Very similar to Jaccard results. Moderately high stress (0.15)

eDNA Ordinations Summary

The ordination that explained the most variance in the eDNA dataset was the PCoA using the Bray-Curtis dissimilarity matrix after Hellinger transformation. This is similar to the approach presented in Lacoursière‐Roussel et al. 2018. Use this representation going forward.

• Next: fit environmental vectors to this ordination to see which can be possibly explain some of the variation among samples and among species.

PCoA with Environmental Variables

Recreate, in 2D, the first two axes of the ordination (PCoA with Bray distance matrx/ Hellinger transformation) and use envfit from vegan to test and fit environmental variables.

If not making 3D plots, can do this directly in phyloseq (eg. https://www.gdc-docs.ethz.ch/MDA/handouts/MDA20_PhyloseqFormation_Mahendra_Mariadassou.pdf)

pcoa_bray <- wcmdscale(bray_dmat, eig = TRUE, add = "cailliez")

# trim metadata to remove samples that were removed during QC
metadata_ordinations <- metadata[metadata$SampleID %in% sample_data(ps_hellinger)$SampleID,]

# and remove repetitive metadata variables
metadata_ordinations <- select(metadata_ordinations, -"Year.Trawl#", -Date, -Month, -Year)

# fit environmental factors and save stats output
pcoa_bray_envfit <- envfit(pcoa_bray, metadata_ordinations, permutations = 1000)
capture.output(pcoa_bray_envfit, file = "stats_results/pcoa_bray_envfit.txt")

# Signficant variables include Datecode (p = 0.03197), Station (p = 0.01898), and, less significantly, Habitat (p = 0.08192)
# Make each of the interesting variables (Datecode and Habitat) their own ordination variables for plotting
pcoa_bray_envfit_datecode <- envfit(pcoa_bray~Datecode, metadata_ordinations, permutations = 1000)
pcoa_bray_envfit_habitat <- envfit(pcoa_bray~Habitat, metadata_ordinations, permutations = 1000)


# plot sig variables, Habitat (qual) and Date (quant) ontop of PCoA
ordiplot(pcoa_bray, type = "points")
with(metadata_ordinations, ordihull(pcoa_bray, Habitat))
plot(pcoa_bray_envfit_datecode, p.max = 0.1)


ordiplot(pcoa_bray, type = "points")
with(metadata_ordinations, plot(scores(pcoa_bray, display='sites'), # identifies coordinates
pch=c(19,17), col=brewer.pal(11,'Paired'), # assign symbols, colours
xlab='Dim1', ylab='Dim2')) #

## STOPPED HERE MAY 5TH. Customize plot above. Colors and shapes are prob wrong. Add ordihull/ env vector ontop. Fix color palette to match other plots

—— Fix below after plot above is fixed- Cant use phyloseqs pcoa ordination with cailliez correction

Check how samples differ in the ordination according to different environmental variables

Bayside

plot_ordination(ps_hellinger, ps_hellinger_bray_pcoa, color = "Bayside") +
  geom_point(size = 4) +
  stat_ellipse(aes(group = Bayside)) +
  ggtitle('Bray Curtis PCoA') +
  coord_fixed(ratio = 1) +
  theme_bw()

Summary: There is overlap of the two, but there are also many EAST samples that fall outside and do no look similar to WEST samples. The transition correlates with axis 2. The WEST samples are more closely clustered together than EAST samples.

Habitat

plot_ordination(ps_hellinger, ps_hellinger_bray_pcoa, color = "Habitat") +
  geom_point(size = 4) +
  stat_ellipse(aes(group = Habitat)) +
  ggtitle('Bray Curtis PCoA') +
  coord_fixed(ratio = 1) +
  theme_bw()

Summary there doesn’t seem to be any effect of habitat type

Date

plot_ordination(ps_hellinger, ps_hellinger_bray_pcoa, color = "Date") +
  geom_point(size = 4) +
  stat_ellipse(aes(group = Date)) +
  ggtitle('Bray Curtis PCoA') +
  coord_fixed(ratio = 1) +
  theme_bw()

Summary There seems to be a continuous transition from July 22 to Sept. 2 but isn’t parallel to either axis 1 or 2.

Vector fitting of numeric variables

# vegan doesn't do a pcoa. try cmdscale from base R on the bray curtis distance matrix (after hellinger transformation)
pcoa <- wcmdscale(bray_dmat, eig = TRUE)

eigenvals(pcoa) %>%
  summary() -> ev

ev

Importance of components:
                        [,1]   [,2]   [,3]    [,4]    [,5]    [,6]    [,7]    [,8]
Eigenvalue            4.2258 2.1784 1.8551 1.01077 0.78507 0.74604 0.57803 0.49798
Proportion Explained  0.2769 0.1428 0.1216 0.06624 0.05145 0.04889 0.03788 0.03263
Cumulative Proportion     NA     NA     NA      NA      NA      NA      NA      NA
                         [,9]  [,10]   [,11]   [,12]   [,13]   [,14]  [,15]  [,16]
Eigenvalue            0.48126 0.4089 0.35895 0.32230 0.29289 0.25365 0.2198 0.1816
Proportion Explained  0.03154 0.0268 0.02352 0.02112 0.01919 0.01662 0.0144 0.0119
Cumulative Proportion      NA     NA      NA      NA      NA      NA     NA     NA
                        [,17]   [,18]    [,19]    [,20]    [,21]    [,22]   [,23]
Eigenvalue            0.15789 0.15153 0.129958 0.120610 0.115693 0.101967 0.08697
Proportion Explained  0.01035 0.00993 0.008517 0.007904 0.007582 0.006682 0.00570
Cumulative Proportion      NA      NA       NA       NA       NA       NA      NA
                        [,24]    [,25]    [,26]    [,27]    [,28]    [,29]    [,30]
Eigenvalue            0.07843 0.069186 0.060555 0.054821 0.051491 0.036271 0.029569
Proportion Explained  0.00514 0.004534 0.003968 0.003593 0.003374 0.002377 0.001938
Cumulative Proportion      NA       NA       NA       NA       NA       NA       NA
                         [,31]    [,32]    [,33]    [,34]     [,35]     [,36]      [,37]
Eigenvalue            0.024343 0.023518 0.018367 0.013871 0.0077006 0.0052630 -2.106e-05
Proportion Explained  0.001595 0.001541 0.001204 0.000909 0.0005046 0.0003449  1.380e-06
Cumulative Proportion       NA       NA       NA       NA        NA        NA         NA
                           [,38]      [,39]     [,40]     [,41]     [,42]     [,43]
Eigenvalue            -0.0077983 -0.0101807 -0.017069 -0.027714 -0.037663 -0.046618
Proportion Explained   0.0005111  0.0006672  0.001119  0.001816  0.002468  0.003055
Cumulative Proportion         NA         NA        NA        NA        NA        NA
                          [,44]     [,45]    [,46]
Eigenvalue            -0.060747 -0.100417 -0.16686
Proportion Explained   0.003981  0.006581  0.01093
Cumulative Proportion        NA        NA       NA